A Scaled q-Rung Orthopair Fuzzy MCDM Framework for Optimal Municipal Solid Waste Management in Pune, India

Selecting optimal Municipal Solid Waste (MSW) management technologies is a complex Multi-Criteria Decision-Making (MCDM) challenge under uncertainty. While q-Rung Orthopair Fuzzy Sets (q-ROFS) are powerful, they cannot capture asymmetric expert confidence. The recently proposed q-Rung Linear Diophantine Fuzzy Sets (q-RLDFS) attempt to address this, but are undermined by restrictive boundary conditions and a flawed score function that leads to illogical ranking reversals. To overcome this, we introduce a novel Scaled q-Rung Orthopair Fuzzy Set (Sq-ROFS), which incorporates independent scaling parameters and revised boundaries. A robust, logically consistent score function is developed to eliminate prior inconsistencies. We also adapt Yager-based aggregation operators to the Sq-ROFS context, including Weighted Average (Sq-ROFYWA) and Weighted Geometric (Sq-ROFYWG) operators and their ordered variants. The framework's effectiveness is validated through a case study on MSW technology selection in Pune, India. The results identify Refuse-Derived Fuel (RDF) and Co-processing as the most suitable strategy, prioritizing high-volume processing and industrial integration. This finding is supported by extensive sensitivity and comparative analyses. This research provides a practical, scalable tool for urban waste management and strengthens fuzzy MCDM theory by ensuring reliable modeling under uncertainty.